The Digital Cell

If you are a cell biologist, you will have noticed the change in emphasis in our field.

At one time, cell biology papers were – in the main – qualitative. Micrographs of “representative cells”, western blots of a “typical experiment”… This descriptive style gave way to more quantitative approaches, converting observations into numbers that could be objectively assessed. More recently, as technology advanced, computing power increased and data sets became more complex, we have seen larger scale analysis, modelling, and automation begin to take centre stage.

This change in emphasis encompasses several areas including (in no particular order):

Statistical analysis

Image analysis

Programming

Automation allowing analysis at scale

Reproducibility

Version control

Data storage, archiving and accessing large datasets

Electronic lab notebooks

Computer vision and machine learning

Prospective and retrospective modelling

Mathematics and physics

The application of these areas is not new to biology and has been worked on extensively for years in certain areas. Perhaps most obviously by groups that identified themselves as “systems biologists”, “computational biologists”, and people working on large-scale cell biology projects. My feeling is that these methods have now permeated mainstream (read: small-scale) cell biology to such an extent that any groups that want to do cell biology in the future have to adapt in order to survive. It will change the skills that we look for when recruiting and it will shape the cell biologists of the future. Other fields such as biophysics and neuroscience are further through this change, while others have yet to begin. It is an exciting time to be a biologist.

I’m planning to post occasionally about the way that our cell biology research group is working on these issues: our solutions and our problems.

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3 thoughts on “The Digital Cell”

I think this is a central issue in modern Biology and I am looking forward to future posts on this matter.
One concern of mine is that real knowledge in quantitative methods is scarce and yet as it permeates mainstream biology, there is a lack of prepared peer reviewers.
My other and much bigger concern is – when is all this going to get into undergraduate Biology education? And I am not talking about bioinformatics. Learning ‘bioinformatics’ is worthless if you do not know statistics, same as you cannot learn about modelling without maths/physics. Programming is like language: you can learn to use it but, per se, it does not make your ideas better.

Thanks for the comment Joaquin. I share your concerns. I know Universities where cutting Mathematics/Statistics modules from our Biology courses has been considered. The reason: “the students find it tough” and “the students don’t do well on it” and “it brings down our satisfaction score”!